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Tier-1 brand, metro location, and attractive ML/AI role with broad skill requirements increase competition.
ML/AI and cloud skills are transferable across industries but still require domain-specific experience, so medium sensitivity.
Multiple mandatory ML, LLM, cloud, containerization and platform tooling requirements imply moderately strict filtering.
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Develop and deploy AI/ML models using Python and ML frameworks like TensorFlow, PyTorch, and Scikit-learn.
Work with large language models (LLMs), generative AI technologies, and perform prompt engineering.
Build and manage AI infrastructure components using cloud AI services (Azure AI, AWS SageMaker, GCP AI), containerization (Docker, Kubernetes), and orchestration tools (MLflow, Kubeflow, Airflow).
Strong programming skills in Python; knowledge of Java/Scala is optional.
Experience with ML frameworks including TensorFlow, PyTorch, and Scikit-learn.
Hands-on experience with cloud AI platforms such as Azure AI, AWS SageMaker, or GCP AI.
Bachelor of Engineering degree.
Experienced in implementing end-to-end AI solutions involving large-scale data processing with tools like Spark, Hadoop, Pandas.
Comfortable architecting microservices and REST APIs for AI application deployment.
Familiar with containerization and ML workflow orchestration to streamline model training and deployment.